theory and practice in wsns- bridging the gap dr. elena gaura [email protected]...
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Theory and Practice in WSNs- Bridging the gapDr. Elena Gaura
[email protected]@coventry.ac.uk
www.coventry.ac.uk/researchnet/cogentcomputing
www.cogentcomputing.org
Introducing myself
Research- Assistant Brunel Univ.
Researcher - Rutherford Appleton Research Lab.
Ph.D– Coventry 1998- 1999•Closed loop control for MEMS sensors•AI+ MEMS, design/implementation towards measurement quality improvement (static & dynamic)
…thinking of smart/intelligent sensors ever since…but together with Computer Scientists
Senior Lecturer in CS Coventry 1999 -2005
Reader in Pervasive Computing- 2006
Director of Cogent Computing ARC- since Jan. 2006
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Output voltage [V]
Smart sensingResearch directions– many possibilities• A/D design boundaries
• Ultra-low power/Harsh env.
• New apps/new sensors
• MEMS/NEMS integration
• particularly networked systems
• piggy back on technological advances
– better or
– newly enabled measurement
Sensor networks
• rich motivational set
• good research niches
Multi-sensor systems - wired or wireless
Systems of sensors – a winning card
WSNs – research motivation
Start point:-Smart Dust (1998) – Pister ($35,000) vision of “millions of tiny wireless sensors (motes) which would fit on the head of a pin”
-sharing “intelligent” systems features (self –x) pushed to XLscale – millions of synchronized, networked, collaborative components
Today:-Dust Networks - $30 mil venture (2006);-TinyOS – the choice for 10000 developers-make the news and popular press- fashion accessory & easy lobbying- big spenders have committed already (BP, Honeywell, IBM, HP)-technologies matured (digital, wireless, sensors)-first working prototypes;-getting towards “out of the lab”-social scientists are getting ready!
Attention!Your spatio-temporal activities are recoded and analyzed by the 20000 sensors wide campus net
WSNs –motivation
Market forecast:
2014- $50bil. , $7bil in 2010 (2004)
2014- $5-7 bil. sales (conservative)
2011-$1.6 bil. smart metering/ demand response
Industrial Markets- old and new; mostly wired
replacements; generally continuous monitoring systems with “data-made-easy” features and internet connected
Prompted by regulations and drive towards process efficiency or else…
the “cement motes” from Xsilogy come with 30 min warranty!
Connecting 466 foil strain gages to a wing box
Invensys asked a Nabisco executive what was the most important thing he wanted to know. The reply came without a moment's delay: "I'd like to know the moisture content at the centre of the cookie when it reaches the middle of the oven."
Research: mainly newly enabled applications; “macroscopes”; adventurous money savings ideas
Infineon tyre sensor
WSNs – the motivational square
Practical, application oriented research and deployments
Theoretical research for large scale networks
Visions
Industrial needs
Research space
Research space
Making the most out of a bad situation
Commercial endeavours
Internet able Microclimate, soil moisture, disease monitoring
Research/Adoption roadblocks
Largest part of community
…forget about throwing them from the back of that plane!...
WSNs – the motivational square
Practical, application oriented research and deployments
Theoretical research for large scale networks
Visions
Industrial needs
Research space
Research space
Research/Adoption roadblocks
Largest part of community…enable throwing them from the back of that plane!...
WSN - Ready to get out of the lab
?Past and current deployments – NO
- Mostly pilot studies
- Very low yield
- “Hacked” designs
- Too tailored
- Small nets
Either or:-Device (miniaturization)-System (networking)
No opportunity to apply deployment lessons to the same problem/application
WSN – theoretical wonders
- Scoping of large scale applications
- Complex problems solved for individual functional components/services
- Theoretical proofs and simulation only
- Lack of integrative work
1. Dust size- mm cube
2. Unplanned deployment3. Distributed
4. Millions of5. Re-configurable nets6. Self-healing7. Scalable8. Autonomous9. Information systems10.Collaborative decisions
1. Stack of quarters & miniaturization vs mote life trade-off2. Planned, carefully measured; ID based3. Gateway based – centrally controlled; backboned4. Hundreds at most (ExScal)5. Hard coded6. Prone to failure (more than 50% usually)7. Only through complete re-design8. Tightly controlled9. Data acquisition – relay to base10. Central post processing
Visions led SENSE and SEND
Cogent research
Facilitates the migration of pervasive sensing from future potential to present success
Design space
•Care for the un-expert user – “beyond data collection systems”
•Robustness, fault tolerance
•Long life – across system layers and system components- in network processing &distribution
•Maintenance free systems – scalability, remote programming &generic components/ infrastructure
VLS networks as
Scientific instruments Permanent monitoring fixtures
“The network is the sensor”
Cogent research
The problems:-point measurements reporting often outside the scope of deployment
-time-space link implied as crucial
-user needs global and/or change/event driven information as deployment outcome
Possible solutions:-In-network information interpretation
-Robustness of information - cross-layer design & top down, integration, distribution
-Optimized query-able systems
Design for re-use
Don’t re-invent the wheel
Design “big” to successfully go “small””
Hang on to the deployment expertise
Cogent Staff and students PhD Students
Tessa Daniel [email protected]:Applicative Query Mechanisms; Information Extraction in Wireless Sensor Networks.
Mike Allen [email protected]:Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing.
John Kemp [email protected]:Advanced Sensing; Sensing Visualisation Systems.
Lee Booi Lim Expertise:Networking; Embedded Systems
Dan Goldsmith [email protected]:Middleware design and test-beds for WSNs
Dr Elena Gaura [email protected]:Advanced Sensing; Advanced Measurement Systems; Ambient Intelligence; Design and Deployment of Wireless Sensor Networks; Distributed Embedded Sensing; Intelligent Sensors; Mapping Services for Wireless Sensor Networks; MEMS Sensors
Dr James Brusey [email protected]:Industrial Robotics and Automation; Machine Learning; RFID; Sensing Visualisation Systems.
Achieving the goals
Platforms & tools- towards the “big” mote
The Gumstix –FFMC
•400MHz xScale processor,•16Mb Ram & 64Mb persistent storage
•on-board Bluetooth; + ZigBee + WiFi•add-on boards expanding capability•allows custom built sensing modules
•full Linux kernel - ease of use/debug•generic- wide range of applications
Tools
-SenSOR – in house algorithmic simulator-HW/SW co-simulator/ rapid deployment tool-NS2
SenSor
Projects- VLS WSN design features
• designing for information visualization - Field sensing – Mapping
• designing for robustness and long life - Fault Detection and management
• designing for information extraction- Complex Querying
• designing for practical applications -BAN and Bioacoustic Monitoring
• designing for robust services support
Designing for practical applications
BAN Bioacoustic monitoringThe problems:
•Robustness of deployment
•Technologies Integration
•Fitness for purpose
•Non-experts will use it!!!
End-to-end system design approach
Deborah Estrin (UCLA, CENS)
Lewis Girod (MIT, CSAIL),
EPSRC &
Industry
1. Nodes detect event of interest, send audio data back to sink over wireless channel
2. Sink processes and fuses detections to estimate position
3. Scientist uses position estimate to direct attention for observation
Designing for practical applications
BAN Bioacoustic monitoringThe problems:
•Robustness of deployment
•Technologies Integration
•Fitness for purpose
•Non-experts will use it!!!
End-to-end system design approach
Deborah Estrin (UCLA, CENS)
Lewis Girod (MIT, CSAIL), EPSRC & Industry
1. Nodes detect event of interest, send audio data back to sink over wireless channel
2. Sink processes and fuses detections to estimate position
3. Scientist uses position estimate to direct attention for observation
Designing for information extraction
Complex Query Processing Approach
The problem:
• very large data sets from very small networks
• (8 hours continuous recording @ 48KHz = 10GB/Node)
• retrieval of data undesirable and inefficient (bandwidth/energy constraints)
Possible solution: Data reduction to information IN NETWORK
• information abstraction- essential to any practical usage of large intelligent sensor networks
• enables the user to formulate complex queries (“qualitative” synergy between semantic groupings of points)
• Incorporate space and time
Most networks are aimed to be “information” tools
1. Nodes detect, send data back to sink over wireless channel
2. Sink processes fuses detections
3. Scientist uses information to direct attention
Designing for information visualization
Problems:• follow on from info extraction • field sensing lends itself to it• difficult to bring down the macroscale
research due to resources constraints• app artefacts would be useful-
isophlets?
Possible solution• customize macro to fit at micro scale• make heavy use of the information
extraction strategy and supporting data routing mechanics
• clever interpolation and data fusion• distributed storage (semantically) of
compressed data
Give the user a global real-time view
and a zooming tool!Information mapping
Designing for robustness and long life
SENSOR 2S2
DIAGNOSTIC NETWORK 2
TDTD Healthy/
FaultyS2(k)
Sensor output(measured
acceleration)
Input acceleration for Sensor 2
a2(k)
S2(k) S2(k-1) S2(k-2)
SELF-DIAGNOSIS SENSOR MODULE – SENSOR2
SENSOR 1S1
DIAGNOSTIC NETWORK 1
TDTD Healthy/
FaultyS1(k)
Sensor output(measured
acceleration)
Input acceleration for Sensor 1
a1(k)
S1(k) S1(k-1) S1(k-2)
SELF-DIAGNOSIS SENSOR MODULE – SENSOR1
S3(k) S3(k-1) S3(k-2)
Figure 4: Block diagrams of two neighboring self-diagnosis sensors (a1(k) = a2(k), in this study)
Figure 6: Diagnostic network performance
…network life-time, reliability and “quality of service” part of the design spec…
The solution: WSN fault management framework
• ensure sensing coverage• ensure connectivity coverage• ensure QoS• longer network lifetime,
fidelity/throughput of data, timeliness of responses
Problems:
•Harsh environments/ unattended operation
•HW defects – common
•Wireless comms - unreliable
•Limited power resources
•Dynamic network topology - lost sensing data, connectivity coverage
Fault Detection and management
Designing for robust services support
Problems:- new deployments/application areas - enabled
through mapping, querying, localization, fault detection, etc
- low maintenance, industrial strength but light-weight WSNs are likely to be needed
- usability+ maintainability + deployability- already an issue
Possible solutions
• have your tools ready
• develop, deploy, test and re-design
• distributed simulation-SenSor
– lightweight execution environment for SenSor simulations
– contains the full functionality of SenSor– code transfer development to hardware -
'one-click' approach to code deployment
Putting it all together- testbed
Closing
• SenSor – Open Source• Videos and demos
– www.cogentcomputing.org
• Industry CDROM:– www.cogentcomputing.org/cds/distributing/
A source localization query
• Aim: collaboratively detect and estimate the location of an event of interest, showing results in a timely on-line manner to direct scientist’s attention
1. Nodes detect event of interest, send audio data back to sink over wireless channel
2. Sink processes and fuses detections to estimate position
3. Scientist uses position estimate to direct attention for observation
Scope
• Complete end-to-end system whose performance can be readily evaluated
• Motivate with several bioacoustics applications• General components developed (networking,
dissemination, visualisation)• Automate well-understood parts of system
(localization, routing, timesync)• User interaction to deal with less well-
understood aspects:– Reconfigure system based on in-field feedback/obs.– Use on-line output of system to direct observation
